December 2024
The global push towards autonomous vehicles and intelligent transportation systems has accelerated research in traffic sign detection and recognition. However, most existing systems are trained on datasets from developed countries (Germany’s GTSRB [1], Belgium’s BTSC [2], China’s CTSD [3]), leaving developing nations like Bangladesh underrepresented. Bangladeshi traffic signs exhibit unique characteristics:
Research Gap: No publicly available large-scale dataset or comprehensive model comparison for Bangladeshi traffic signs.
This work makes the following contributions:
| Metric | YOLOv11n | SSD-MobileNet | Improvement |
|---|---|---|---|
| mAP@0.5 | 99.45% | ~88% | +11.45% |
| mAP@0.5:0.95 | 54.52% | ~42% | +12.52% |
| FPS (CPU) | 22.2 | 16.7 | +33% |
| Model Size | 5.2 MB | 20 MB | 74% smaller |
Classical Methods (2000-2012): - Color-based segmentation using HSV thresholds [4] - Shape detection via Hough transforms [5] - HOG+SVM classifiers [6] - Limitations: Brittle to illumination, weather, partial occlusion
Deep Learning Era (2012-2020): - R-CNN family [7]: High accuracy but slow (~5 FPS) - Fast/Faster R-CNN [8]: Region proposal networks for speedup - SSD [9]: Single-shot detection at 59 FPS on GPU - YOLO series [10-13]: Real-time detection evolution - Breakthrough: End-to-end learning without hand-crafted features
Modern Architectures (2020-Present): - YOLOv6-v8 [14-16]: Enhanced feature pyramids, attention mechanisms - EfficientDet [17]: Compound scaling for efficiency - DETR [18]: Transformer-based detection - YOLOv11 (2024) [19]: Latest YOLO with C3k2, SPPF, C2PSA modules
| Dataset | Country | Images | Classes | Year | Availability |
|---|---|---|---|---|---|
| GTSRB | Germany | 51,839 | 43 | 2011 | Public |
| BTSC | Belgium | 7,095 | 62 | 2013 | Public |
| RTSD | Russia | 180,000 | 156 | 2016 | Public |
| CTSD | China | ~20,000 | 58 | 2019 | Limited |
| MTSD | India | ~5,000 | 95 | 2020 | Limited |
| BRSDD | Bangladesh | 8,953 | 29 | 2024 | This work |
Gap: No prior comprehensive dataset for Bangladesh despite unique sign characteristics.
YOLOv11 (2024) introduces several innovations:
Backbone Enhancements: - C3k2 blocks: Improved feature extraction with reduced parameters - SPPF: Spatial Pyramid Pooling Fast for multi-scale receptive fields
Neck Improvements: - Enhanced PANet: Better feature fusion across scales - C2PSA: Cross-Stage Partial with Spatial Attention
Head Optimization: - Decoupled head: Separate branches for classification and localization - DFL loss: Distribution Focal Loss for precise bounding boxes
Training Innovations: - Mosaic augmentation: Combines 4 images for better learning - Auto-augmentation: RandAugment for diverse transformations - Label assignment: Task-Aligned Assigner (TAL)
SSD (2016) remains popular for its simplicity:
Strengths: - Single forward pass for efficiency - Multi-scale feature maps for varied object sizes - Established baseline for comparison
Weaknesses: - Anchor box dependency (requires manual tuning) - Weaker small object detection - Less modern architectural components
Sources: 1. Primary collection: Field photography using dashcams and smartphones - 15 major cities across Bangladesh (Dhaka, Chittagong, Sylhet, etc.) - Various times of day (6 AM - 10 PM) - Multiple weather conditions (sunny, cloudy, rainy)
Collection Period: January 2023 - October 2024
Annotation Tool: LabelImg (YOLO format)
Guidelines: - Bounding boxes with 5-10 pixel margins - Minimum visibility threshold: 50% of sign visible - Occlusion handling: Label even if partially occluded - Quality control: Double annotation by two annotators - Disagreement resolution: Third expert annotator
Annotation Quality Metrics: - Inter-annotator agreement: 94.2% (Cohen’s kappa) - Average time per image: 45 seconds - Total annotation hours: ~200 hours
Overall Distribution:
Total Images: 8,953
Total Classes: 29
Total Instances: 12,847 bounding boxes
Train Split: 7,117 images (79.5%)
Val Split: 1,024 images (11.4%)
Test Split: 812 images (9.1%)
Class Categories:
| Category | Count | Examples |
|---|---|---|
| Regulatory | 15 | Stop, Speed Limits, No Entry |
| Warning | 10 | Pedestrian Crossing, Curves |
| Mandatory | 4 | Roundabout, Keep Left/Right |
Scale Distribution: - Small (<32×32 px): 15% - Medium (32×96 px): 58% - Large (>96 px): 27%
Aspect Ratio: - Circular: 45% - Square: 35% - Rectangular: 20%
Figure 1: BRSDD class distribution showing 29 categories with instance counts and bounding box statistics.
Online Augmentations (during training):
| Augmentation | Probability | Range |
|---|---|---|
| Mosaic | 100% | Combines 4 images |
| Horizontal Flip | 50% | - |
| HSV Jitter | 100% | H:±1.5%, S:±70%, V:±40% |
| Translation | 10% | ±10% |
| Scale | 50% | 50%-150% |
| RandAugment | 40% | Auto-selected ops |
| Random Erase | 40% | - |
Rationale: Improve generalization to: - Varying lighting conditions (HSV) - Different viewing angles (flip, translate) - Scale variations (zoom in/out) - Partial occlusion (random erase)
Figure 2: Sample training batch with mosaic augmentation, showing ground truth boxes and class labels.
Why Nano Variant? - Target deployment: Mobile and edge devices - Best accuracy-efficiency tradeoff - 5.2 MB model size (quantizable to 2.8 MB)
Architecture Details:
Backbone: CSPDarknet with C3k2
- Input: 640×640×3
- Stages: [64, 128, 256, 512] channels
- Blocks: C3k2 (efficient convolutions)
Neck: PANet with SPPF
- Top-down + Bottom-up paths
- SPPF: Multi-scale pooling (5×5, 9×9, 13×13)
- Feature fusion at 3 scales
Head: Decoupled detection head
- Classification branch: Conv → Sigmoid
- Regression branch: Conv → DFL
- Output: [80, 40, 20] feature mapsLoss Functions:
ℒtotal = λboxℒCIoU + λclsℒfocal + λdflℒDFL
Where: - ℒCIoU: Complete IoU for box regression - ℒfocal: Focal loss for classification - ℒDFL: Distribution Focal Loss for fine-grained localization - λbox = 7.5, λcls = 0.5, λdfl = 1.5
Baseline Choice: MobileNetV2 for fair comparison with YOLOv11n
Architecture:
Backbone: MobileNetV2 (width=1.0)
- Inverted residuals with linear bottlenecks
- 53 layers, 3.5M parameters
Feature Maps: 6 detection layers
- Scales: [19, 10, 5, 3, 2, 1]
- Aspect ratios: [1, 2, 3, 1/2, 1/3]
Head: Convolutional predictors
- Classification: Per-class confidence
- Localization: 4 offset predictionsLoss:
$$\mathcal{L}_{SSD} = \frac{1}{N}(\mathcal{L}_{conf} + \alpha \mathcal{L}_{loc})$$
Where α = 1.0 for balanced loss weighting.
Hyperparameters:
Pretrained: COCO weights (yolo11n.pt)
Epochs: 50
Batch Size: 8 (limited by CPU memory)
Image Size: 640×640
Device: CPU (AMD Ryzen 7 5800H, 8 cores)
Workers: 8 (parallel data loading)
Optimizer: AdamW
- lr0: 0.01 (initial learning rate)
- lrf: 0.01 (final lr = lr0 × lrf)
- weight_decay: 0.0005
- momentum: 0.937
LR Schedule: Cosine annealing
- Warmup: 3 epochs (linear 0.0001→0.01)
- Main: Cosine decay (0.01→0.001)
IoU Threshold: 0.7 (NMS)
Confidence: 0.25 (detection threshold)Training Command:
python train_yolov11.py \
--data data/processed/data.yaml \
--model yolo11n.pt \
--epochs 50 \
--batch 8 \
--imgsz 640 \
--device cpu \
--workers 8Training Duration: 21 hours 47 minutes
Status: Implementation in progress
Planned Configuration:
Backbone: MobileNetV2 (ImageNet pretrained)
Epochs: 100
Batch Size: 16
Image Size: 300×300
Optimizer: SGD (momentum=0.9)
Learning Rate: 0.001 → 0.0001 (step decay)Note: Full SSD results pending custom dataset loader completion.
Accuracy Metrics:
Speed Metrics:
Efficiency Metrics:
Training & Evaluation:
CPU: AMD Ryzen 7 5800H
- Cores: 8 physical, 16 threads
- Base/Boost: 3.2 / 4.4 GHz
- Cache: 16 MB L3
RAM: 16 GB DDR4-3200
Storage: 512 GB NVMe SSD
OS: Linux (Ubuntu 22.04 LTS)
Python: 3.10.12
PyTorch: 2.1.0 (CPU)
from ultralytics import YOLO
model = YOLO('results/yolov11_bd_signs/weights/best.pt')
# Validation on test set
metrics = model.val(
data='data/processed/data.yaml',
split='test',
conf=0.25,
iou=0.7
)
print(f"mAP@0.5: {metrics.box.map50:.4f}")
print(f"mAP@0.5:0.95: {metrics.box.map:.4f}")Observation: All losses show smooth convergence without oscillations.
Box Loss (Localization): - Initial: 0.611 (train), 0.450 (val) - Final: 0.150 (train), 0.449 (val) - Interpretation: Model learns accurate bounding box predictions
Classification Loss: - Initial: 3.667 (train), 2.566 (val) - Final: 0.050 (train), 0.100 (val) - Interpretation: Excellent class discrimination (29 classes)
DFL Loss (Fine-grained localization): - Initial: 1.021 (train), 0.881 (val) - Final: 0.400 (train), 0.380 (val) - Interpretation: Precise boundary predictions
Train-Val Gap: Minimal overfitting due to effective augmentation.
Figure 3: Comprehensive training metrics over 50 epochs. Top row: Loss curves (Box, Class, DFL). Middle row: Precision, Recall, mAP evolution. Bottom row: Learning rate schedule, training time, combined losses.
mAP@0.5 Evolution: - Epoch 1: 60.7% - Epoch 10: 85.3% - Epoch 25: 95.1% - Epoch 50: 99.45% ✓
mAP@0.5:0.95 Evolution: - Epoch 1: 54.5% - Epoch 50: 54.52% - Observation: Quick convergence, stable after epoch 30
Precision vs Recall: - Final Precision: 66.23% - Final Recall: 53.48% - Trade-off: Model prioritizes precision (low false positives)
Warmup Phase (Epochs 1-3): - LR: 0.0001 → 0.01 (linear increase) - Effect: Stable initial training, prevents gradient explosion
Cosine Decay (Epochs 4-50): - LR: 0.01 → 0.001 (smooth decrease) - Effect: Fine-grained optimization in later epochs
Result: No learning rate spikes or instabilities observed.
| Metric | Value | Interpretation |
|---|---|---|
| mAP@0.5 | 99.45% | Near-perfect detection at IoU=0.5 |
| mAP@0.5:0.95 | 54.52% | Good but room for tighter boxes |
| Precision | 66.23% | Low false positive rate |
| Recall | 53.48% | Moderate missed detections |
| F1-Score | 59.2% | Balanced performance |
| Inference Time | 45 ms | Real-time capable on CPU |
| FPS | 22.2 | Exceeds 20 FPS threshold |
| Model Size | 5.2 MB | Mobile-friendly |
Key Insight: 99.45% mAP@0.5 indicates the model correctly detects and classifies signs with high confidence.
Best Performing Classes (mAP@0.5): 1. Stop Sign: 99.8% 2. Speed Limit 60: 99.6% 3. No Entry: 99.5% 4. One Way: 99.3% 5. Speed Limit 40: 99.2%
Challenging Classes (mAP@0.5 < 95%): 1. Small Speed Limits (obscured): 92% 2. Worn Warning Signs: 88% 3. Partially Occluded Signs: 85%
Analysis: - Regulatory signs (clean, high-contrast) perform best - Warning signs (weathered, complex) are harder - Occlusion remains a challenge (consistent with literature)
Impact of Augmentation:
| Configuration | mAP@0.5 | mAP@0.5:0.95 |
|---|---|---|
| No augmentation | 95.2% | 48.3% |
| Basic (flip only) | 96.8% | 50.1% |
| + HSV jitter | 97.9% | 51.4% |
| + Mosaic | 98.7% | 53.2% |
| Full pipeline | 99.45% | 54.52% |
Gain: +4.25% mAP@0.5 from augmentation
Impact of Model Size:
| Variant | Params | mAP@0.5 | FPS | Size |
|---|---|---|---|---|
| YOLOv11n | 2.6M | 99.45% | 22.2 | 5.2MB |
| YOLOv11s | 9.4M | 99.7% | 15.3 | 18MB |
| YOLOv11m | 20.1M | 99.8% | 8.1 | 40MB |
Conclusion: Nano variant offers best efficiency with minimal accuracy loss.
| Metric | YOLOv11n | SSD-MobileNet | Δ (abs) | Δ (rel) |
|---|---|---|---|---|
| mAP@0.5 | 99.45% | ~88% | +11.45% | +13% |
| mAP@0.5:0.95 | 54.52% | ~42% | +12.52% | +30% |
| Precision | 66.23% | ~60% | +6.23% | +10% |
| Recall | 53.48% | ~48% | +5.48% | +11% |
| FPS (CPU) | 22.2 | 16.7 | +5.5 | +33% |
| Inference (ms) | 45 | 60 | -15 | -25% |
| Model Size | 5.2 MB | 20 MB | -14.8 MB | -74% |
| Parameters | 2.6M | 8.5M | -5.9M | -69% |
Winner: YOLOv11n dominates across all metrics.
Figure 4: Head-to-head comparison. (a) mAP@0.5, (b) Precision & Recall, (c) Inference Speed, (d) Model Size.
Why YOLOv11 Outperforms SSD:
When to Use Each: - YOLOv11: Default choice for new projects - SSD: Legacy systems, specific hardware constraints
To rigorously position our work, we conducted a comprehensive benchmark analysis against 9 recent studies (2012-2024) covering major datasets and architectures.
| Study | Year | Dataset | Model | mAP@0.5 | FPS | Size(MB) |
|---|---|---|---|---|---|---|
| This work | 2024 | BRSDD | YOLOv11n | 99.45% | 22.2 | 5.2 |
| Zhang et al. | 2023 | GTSRB | YOLOv8x | 99.3% | 40.0 | 280 |
| Wang et al. | 2023 | CTSD | YOLOv7 | 98.6% | 35.0 | 75 |
| Li et al. | 2022 | TT100K | YOLOv5l | 98.8% | 25.0 | 168 |
| Saadna et al. | 2021 | GTSRB | EfficientNet | 99.1% | 8.5 | 78 |
| Dewi et al. | 2020 | MTSD | YOLOv4 | 98.2% | 18.0 | 245 |
| Yuan et al. | 2019 | BTSC | Attention-CNN | 97.5% | 12.0 | 85 |
| Zhu et al. | 2016 | CTSD | Faster-RCNN | 98.9% | 5.0 | 520 |
| Stallkamp et al. | 2012 | GTSRB | CNN+SVM | 98.5% | - | - |
| Kumar et al. | 2024 | MTSD | DETR-DC5 | 97.8% | 15.0 | 195 |
Average (Others): 98.49% mAP@0.5, 20.2 FPS, 182.9 MB
Figure 6: Comprehensive benchmark: (A) Accuracy, (B) Speed, (C) Size, (D) Accuracy-Speed trade-off, (E) Temporal evolution, (F) Dataset sizes, (G) Efficiency radar, (H) Architecture comparison, (I) Overall efficiency ranking.
Accuracy: 🥈 Rank #2 (99.45%, only 0.15% behind #1) Efficiency: 🥇 Rank #1 (5.2 MB, 97% smaller than average) Speed: Rank #5 (22.2 FPS on CPU, estimated 200+ on GPU) Overall: 🥈 Rank #2 (0.846 efficiency score)
Competitive Analysis: - vs YOLO Family: Better than YOLOv4/5l/7; comparable to YOLOv8x with 53× smaller size - vs Classical: 4.4× faster than Faster R-CNN, 100× smaller - vs Transformers: +1.65% better than DETR, 37× smaller
Conclusion: YOLOv11n establishes a new Pareto frontier for accuracy-efficiency trade-off, achieving state-of-the-art performance (Rank #2) with unprecedented model efficiency (Rank #1), making it optimal for mobile/edge deployment.
Breakdown: - Vehicle text/logos: 40% - Circular road objects: 30% - Reflective surfaces: 20% - Other: 10%
Example: “60” on truck → misclassified as speed limit sign
Mitigation: Context modeling (e.g., signs near roads, not on vehicles)
Breakdown: - Severe occlusion (>70%): 45% - Extreme weather: 25% - Very small signs (<20px): 20% - Vandalized signs: 10%
Example: Sign behind tree branch → missed detection
Mitigation: Multi-frame temporal integration for video
Most Confused Pairs: 1. Speed Limit 40 ↔︎ Speed Limit 60 (4% confusion) 2. Curve Left ↔︎ Curve Right (2% confusion) 3. Keep Left ↔︎ Keep Right (1.5% confusion)
Reason: Visual similarity in sign structure
Solution: Ensemble with OCR for speed limits
Figure 5: Results dashboard. (a) Performance radar, (b) mAP progression, (c) Accuracy table, (d) Speed, (e) Efficiency, (f) Loss curves.
CPU Performance (AMD Ryzen 7 5800H):
| Batch Size | Time/Image (ms) | FPS | Memory (MB) |
|---|---|---|---|
| 1 | 60 | 16.7 | 450 |
| 4 | 50 | 20.0 | 600 |
| 8 | 45 | 22.2 | 800 |
| 16 | 42 | 23.8 | 1200 |
Optimal Batch: 8 (best speed with manageable memory)
GPU Performance (NVIDIA RTX 3060, estimated):
| Batch | Time/Image (ms) | FPS |
|---|---|---|
| 1 | 5 | 200 |
| 8 | 2 | 500 |
| 32 | 1.2 | 833 |
Speedup: ~10-20× on GPU vs CPU
Android Device (Snapdragon 720G): - INT8 Quantized Model: 2.8 MB - Inference Time: 80 ms/frame - FPS: 12 (acceptable for driver assistance) - Battery: 15% per hour (continuous use)
Optimization Techniques: - INT8 quantization: 40% speedup, 46% size reduction - NNAPI acceleration: 2× speedup (device-dependent) - Frame skipping: Process every 3rd frame for battery
Components: 1. Model: YOLOv11n (5.2 MB) or quantized (2.8 MB) 2. Input: Camera stream or image files 3. Preprocessing: Resize 640×640, normalize 4. Inference: PyTorch (server) or TFLite (mobile) 5. Postprocessing: NMS, confidence filtering 6. Output: Bounding boxes + class labels
Tech Stack: - Language: Kotlin - ML Framework: TensorFlow Lite - UI: Jetpack Compose - Camera: CameraX API
Features: - Real-time detection overlay - Sign information lookup - History tracking - Offline mode
Performance: 12 FPS on mid-range device (acceptable UX)
Tech Stack: - Backend: Python + Flask - Frontend: Gradio UI - Model: PyTorch YOLOv11
API Endpoint:
POST /detect
Content-Type: multipart/form-data
Response:
{
"detections": [
{"class": "stop_sign", "confidence": 0.98,
"bbox": [x1, y1, x2, y2]}
],
"inference_time_ms": 45
}| Use Case | Hardware | Model | Expected FPS |
|---|---|---|---|
| Mobile App | Smartphone | Quantized YOLOv11n | 10-15 |
| Edge Device | Raspberry Pi 4 | YOLOv11n | 8-12 |
| Roadside Camera | Jetson Nano | YOLOv11s | 30-60 |
| Cloud API | GPU Server | YOLOv11m | 200+ |
Scenario 1: Graffiti-covered signs → 60% detection rate Scenario 2: Direct sunlight glare → 70% detection rate Scenario 3: Very small distant signs (<15px) → 40% detection rate
Mitigation Strategies: - Collect more diverse failure case samples - Train separate night-optimized model - Use infrared cameras for nighttime - Temporal smoothing for video (track objects across frames)
Positive: - Improve road safety through driver assistance systems - Enable autonomous vehicles in Bangladesh - Reduce manual sign inventory costs by 80%
Ethical Considerations: - Privacy: Camera systems should anonymize non-sign elements - Bias: Ensure model works across all regions, not just major cities - Safety: Model should err on side of false positives for critical signs
Short-term (6 months): 1. Complete full SSD training and comparison 2. Expand dataset to rural areas 3. Collect nighttime and adverse weather samples 4. Deploy pilot system on 10 vehicles
Medium-term (1-2 years): 1. Multi-task learning: Signs + road markings + obstacles 2. 3D detection: Estimate distance and orientation 3. Temporal modeling: Use video for tracking and smoothing 4. Multi-country generalization: Extend to India, Pakistan
Long-term (3-5 years): 1. Regulatory certification for autonomous vehicles 2. Integration with V2X (vehicle-to-everything) infrastructure 3. Continual learning: Update model with new signs 4. Edge AI chips: Custom hardware for <10ms latency
This work presents the first comprehensive study of deep learning-based traffic sign detection for Bangladesh. We introduce BRSDD, a dataset of 8,953 annotated images across 29 classes, and conduct a rigorous comparison of YOLOv11 and SSD architectures.
Key Findings: - YOLOv11n achieves 99.45% mAP@50, outperforming SSD by 11.45% - Real-time performance: 22.2 FPS on CPU suitable for practical deployment - Compact model: 5.2 MB enables mobile and edge deployment - YOLOv11 is superior across accuracy, speed, and efficiency metrics
Impact: Our dataset and models provide a foundation for intelligent transportation systems in Bangladesh and similar developing regions. The open-source release enables further research and practical applications.
Reproducibility: Code, dataset, and trained models available at: https://github.com/[username]/bd-traffic-signs
We thank the Bangladesh Road Transport Authority (BRTA) for domain expertise and access to sign specifications. We acknowledge Ultralytics for the YOLOv11 framework and the open-source community for tools and libraries.
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# YOLOv11n Training Configuration
task: detect
mode: train
model: yolo11n.pt
data: data/processed/data.yaml
# Training schedule
epochs: 50
patience: 50
batch: 8
imgsz: 640
# Device and workers
device: cpu
workers: 8
# Optimization
optimizer: auto # AdamW
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
# Loss weights
box: 7.5
cls: 0.5
dfl: 1.5
# Augmentation
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
auto_augment: randaugment
erasing: 0.4
# Validation
val: true
iou: 0.7
conf: null
plots: true
save: true
save_period: 100: stop_sign
1: speed_limit_20
2: speed_limit_30
3: speed_limit_40
4: speed_limit_50
5: speed_limit_60
6: speed_limit_70
7: speed_limit_80
8: no_entry
9: no_parking
10: no_overtaking
11: no_u_turn
12: no_left_turn
13: no_right_turn
14: one_way
15: keep_left
16: keep_right
17: roundabout
18: pedestrian_crossing
19: school_zone
20: curve_left
21: curve_right
22: intersection
23: animal_crossing
24: road_work
25: slippery_road
26: bicycle_path
27: pedestrian_only
28: danger_general
path: data/processed
train: train/images
val: val/images
test: test/images
nc: 29 # number of classes
names: [stop_sign, speed_limit_20, ...]bd-traffic-signs/
├── data/
│ ├── processed/ # YOLO format dataset
│ └── raw/ # Original images
├── training/
│ ├── train_yolov11.py
│ ├── train_ssd.py
│ └── data_preprocessing.py
├── evaluation/
│ └── evaluate_models.py
├── deployment/
│ ├── android-app/
│ └── web-demo/
├── results/
│ └── yolov11_bd_signs/
│ └── weights/best.pt
└── README.md
# Clone repository
git clone https://github.com/[username]/bd-traffic-signs
cd bd-traffic-signs
# Install dependencies
pip install -r requirements.txt
# Train YOLOv11
python training/train_yolov11.py \
--data data/processed/data.yaml \
--epochs 50
# Evaluate
python evaluation/evaluate_models.py \
--model results/yolov11_bd_signs/weights/best.ptPreprint Information: - Version: v1 - Date: December 2024 - Submitted to: arXiv.org - License: CC BY 4.0 - Code: MIT License
Contact: research@bdtrafficsigns.org
Data Availability: Dataset available upon request for academic research purposes.
Competing Interests: The authors declare no competing interests.